ArticleCommunications biology2021
Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Linking preclinical models with functional imaging and artificial intelligence to decode tumor angiogenesis.Pharmacological reports : PR · 2026Review
- Intravenous extravasation report generation using deep learning, generative artificial intelligence, and visual question answering techniques.Scientific reports · 2026Article
- Real-Time and Spatially Resolved Epigenetic Dynamics Tracking Beyond DNA Methylation via Live-Cell Epigenetic Sensors in 3D Systems.Biosensors · 2026Review
- CDC42-effector interaction inhibitors alter patterns of vessel arborization in skin and tumorsiScience · 2025Article
- Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.Frontiers in medicine · 2025Review
- An in situ depot for the sustained release of a TLR7/8 agonist in combination with a TGFβ inhibitor promotes anti-tumor immune responses.Nature communications · 2024Article
- Progressive changes in the protein expression profile of alveolar septa in early-stage lung adenocarcinoma.International journal of clinical oncology · 2024Article
- Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.Biomedical optics express · 2024Article
- Comparison of micro-CT image enhancement after use of different vascular casting agents.Quantitative imaging in medicine and surgery · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
- Multiplex imaging in immuno-oncology.Journal for immunotherapy of cancer · 2023Review
- Volume imaging to interrogate cancer cell-tumor microenvironment interactions in space and time.Frontiers in immunology · 2023Review
- An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis.Nature communications · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an injected compound, thereby allowing for ex vivo assessment of extravasation into the brain. However, pathological remodeling of tissue microenvironment can affect the efficiency of transcardial perfusion, which has been largely overlooked. We show that, in contrast to healthy vasculature, transcardial perfusion cannot remove an injected compound from the tumor vasculature to a sufficient extent leading to considerable overestimation of compound extravasation. We demonstrate that 3D deep imaging of optically cleared tumor samples overcomes this limitation. We developed two machine learning-based semi-automated image analysis workflows, which provide detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets from optically cleared samples. This methodology provides a precise and comprehensive analysis of extravasation in brain tumors and allows for correlation of extravasation patterns with specific features of the heterogeneous brain tumor vasculature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.